Genetic Algorithm vs Particle Swarm Optimization
These kind of questions cannot be answered without looking at a particular project. Each algorithm has its particular strengths and weaknesses; and trade-offs in terms of use of resources (processing power and/or storage space, for example). If there was an objective answer, then the worse algorithm would surely fall in disuse.

It also depends what you mean by "better". Faster? Better score according to some evaluation measure? More robust (ie works with many diverse data sets)? 

I would recommend looking at both algorithms in more detail, and trying to understand how they work. Then you should be able to find out which best fits your problem.

However, one problem with Particle Swarm Optimisation is that it is not well understood, so you might have to resort to trial-and-error.